Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Predicting causality ascriptions from background knowledge: model and experimental validation
International Journal of Approximate Reasoning
A Comparative Study of Six Formal Models of Causal Ascription
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Making sense as a process emerging from perception–memory interaction: A model
International Journal of Intelligent Systems
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People try to make sense of the usually incomplete reports they receive about events that take place. For doing this, they make use of what they believe the normal course of thing should be. An agent$\textquoteright$s beliefs may be consonant or dissonant with what is reported. For making sense people usually ascribe different types of relations between events. A prototypical example is the ascription of causality between events. The paper proposes a systematic study of consonance and dissonance between beliefs and reports. The approach is shown to be consistent with findings in psychology. An implementation is presented with some illustrative examples.